Overview

Dataset statistics

Number of variables11
Number of observations1446
Missing cells10
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory124.4 KiB
Average record size in memory88.1 B

Variable types

DateTime1
Numeric10

Alerts

MSFT is highly correlated with GOOG and 4 other fieldsHigh correlation
GOOG is highly correlated with MSFT and 3 other fieldsHigh correlation
FB is highly correlated with MSFT and 3 other fieldsHigh correlation
AMZN is highly correlated with MSFT and 3 other fieldsHigh correlation
HP is highly correlated with IBMHigh correlation
IBM is highly correlated with HPHigh correlation
APPLE is highly correlated with MSFT and 4 other fieldsHigh correlation
INTEL is highly correlated with MSFT and 1 other fieldsHigh correlation
MSFT is highly correlated with GOOG and 5 other fieldsHigh correlation
GOOG is highly correlated with MSFT and 4 other fieldsHigh correlation
FB is highly correlated with MSFT and 3 other fieldsHigh correlation
AMZN is highly correlated with MSFT and 3 other fieldsHigh correlation
HP is highly correlated with IBMHigh correlation
IBM is highly correlated with MSFT and 2 other fieldsHigh correlation
APPLE is highly correlated with MSFT and 4 other fieldsHigh correlation
INTEL is highly correlated with MSFT and 3 other fieldsHigh correlation
MSFT is highly correlated with GOOGHigh correlation
GOOG is highly correlated with MSFT and 1 other fieldsHigh correlation
FB is highly correlated with GOOGHigh correlation
MSFT is highly correlated with GOOG and 7 other fieldsHigh correlation
GOOG is highly correlated with MSFT and 7 other fieldsHigh correlation
FB is highly correlated with MSFT and 7 other fieldsHigh correlation
AMZN is highly correlated with MSFT and 6 other fieldsHigh correlation
HP is highly correlated with MSFT and 6 other fieldsHigh correlation
TSLA is highly correlated with MSFT and 7 other fieldsHigh correlation
IBM is highly correlated with MSFT and 7 other fieldsHigh correlation
APPLE is highly correlated with MSFT and 7 other fieldsHigh correlation
INTEL is highly correlated with MSFT and 7 other fieldsHigh correlation
Date has unique values Unique
SMSN has 39 (2.7%) zeros Zeros
HP has 32 (2.2%) zeros Zeros
APPLE has 16 (1.1%) zeros Zeros

Reproduction

Analysis started2022-01-08 11:49:46.297774
Analysis finished2022-01-08 11:50:02.902164
Duration16.6 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Date

UNIQUE

Distinct1446
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
Minimum2016-01-04 00:00:00
Maximum2021-09-29 00:00:00
2022-01-08T12:50:02.975205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:03.119195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MSFT
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1433
Distinct (%)99.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.001138604888
Minimum-0.1329289802
Maximum0.1594534152
Zeros12
Zeros (%)0.8%
Negative792
Negative (%)54.8%
Memory size11.4 KiB
2022-01-08T12:50:03.280166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.1329289802
5-th percentile-0.02420568732
Q1-0.009222371169
median-0.001048601993
Q30.005834426712
95-th percentile0.02614557013
Maximum0.1594534152
Range0.2923823953
Interquartile range (IQR)0.01505679788

Descriptive statistics

Standard deviation0.01697751483
Coefficient of variation (CV)-14.91080445
Kurtosis11.35287271
Mean-0.001138604888
Median Absolute Deviation (MAD)0.007419298498
Skewness0.35378688
Sum-1.645284062
Variance0.0002882360097
MonotonicityNot monotonic
2022-01-08T12:50:03.509165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012
 
0.8%
0.0044843604582
 
0.1%
-0.010872529831
 
0.1%
-0.0076855722321
 
0.1%
-0.0084152464961
 
0.1%
-0.029579068211
 
0.1%
0.0086019906211
 
0.1%
0.014197934681
 
0.1%
-0.037142528121
 
0.1%
0.044072584631
 
0.1%
Other values (1423)1423
98.4%
ValueCountFrequency (%)
-0.13292898021
0.1%
-0.086999122661
0.1%
-0.079122132511
0.1%
-0.072976656191
0.1%
-0.071732414791
0.1%
-0.067976986671
0.1%
-0.066147259181
0.1%
-0.066077770711
0.1%
-0.064418912951
0.1%
-0.062147029081
0.1%
ValueCountFrequency (%)
0.15945341521
0.1%
0.09964166641
0.1%
0.074411409261
0.1%
0.073064344791
0.1%
0.070178453141
0.1%
0.063948684891
0.1%
0.055870199631
0.1%
0.055613839721
0.1%
0.055193940161
0.1%
0.054951548871
0.1%

GOOG
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1445
Distinct (%)100.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.0008915702023
Minimum-0.09937954791
Maximum0.1176672622
Zeros0
Zeros (%)0.0%
Negative798
Negative (%)55.2%
Memory size11.4 KiB
2022-01-08T12:50:03.662202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.09937954791
5-th percentile-0.02372890883
Q1-0.008809504012
median-0.00134712282
Q30.0056614807
95-th percentile0.02577672441
Maximum0.1176672622
Range0.2170468101
Interquartile range (IQR)0.01447098471

Descriptive statistics

Standard deviation0.01643261849
Coefficient of variation (CV)-18.4310988
Kurtosis6.410293319
Mean-0.0008915702023
Median Absolute Deviation (MAD)0.007259848684
Skewness0.317828112
Sum-1.288318942
Variance0.0002700309504
MonotonicityNot monotonic
2022-01-08T12:50:03.801199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0018535602851
 
0.1%
-0.010841357211
 
0.1%
-0.00081836681771
 
0.1%
-0.0078158542631
 
0.1%
-0.0072551354881
 
0.1%
0.024051098931
 
0.1%
-0.00097806669991
 
0.1%
0.026497250731
 
0.1%
0.017445480891
 
0.1%
-0.0070430507921
 
0.1%
Other values (1435)1435
99.2%
ValueCountFrequency (%)
-0.099379547911
0.1%
-0.089855791721
0.1%
-0.083779964811
0.1%
-0.077980701581
0.1%
-0.071353436281
0.1%
-0.071081602941
0.1%
-0.062768647861
0.1%
-0.058215995411
0.1%
-0.052389542841
0.1%
-0.052356240051
0.1%
ValueCountFrequency (%)
0.11766726221
0.1%
0.086307781741
0.1%
0.080089290461
0.1%
0.065935540671
0.1%
0.063015643671
0.1%
0.058155082621
0.1%
0.056178426011
0.1%
0.055405249491
0.1%
0.054644787741
0.1%
0.052083234341
0.1%

FB
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1443
Distinct (%)99.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.0008309138361
Minimum-0.1442859946
Maximum0.2102387012
Zeros3
Zeros (%)0.2%
Negative765
Negative (%)52.9%
Memory size11.4 KiB
2022-01-08T12:50:03.941206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.1442859946
5-th percentile-0.02852447975
Q1-0.01121886839
median-0.001118076957
Q30.007720138706
95-th percentile0.02942366315
Maximum0.2102387012
Range0.3545246959
Interquartile range (IQR)0.0189390071

Descriptive statistics

Standard deviation0.02053041895
Coefficient of variation (CV)-24.70824056
Kurtosis13.84116996
Mean-0.0008309138361
Median Absolute Deviation (MAD)0.009506462794
Skewness0.7401049925
Sum-1.200670493
Variance0.0004214981021
MonotonicityNot monotonic
2022-01-08T12:50:04.077165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
0.2%
0.042168618351
 
0.1%
0.15376905681
 
0.1%
-0.020911220251
 
0.1%
-0.0078485343661
 
0.1%
-0.0074256826941
 
0.1%
0.00093171840991
 
0.1%
-0.02373959351
 
0.1%
0.016509922451
 
0.1%
0.014363715041
 
0.1%
Other values (1433)1433
99.1%
ValueCountFrequency (%)
-0.14428599461
0.1%
-0.10270444391
0.1%
-0.097444225891
0.1%
-0.086739991521
0.1%
-0.083392347031
0.1%
-0.079944183291
0.1%
-0.079017698491
0.1%
-0.078578613941
0.1%
-0.078416826541
0.1%
-0.071152576491
0.1%
ValueCountFrequency (%)
0.21023870121
0.1%
0.15376905681
0.1%
0.097209326321
0.1%
0.086826204271
0.1%
0.078020978791
0.1%
0.075297373641
0.1%
0.070097181171
0.1%
0.066141196341
0.1%
0.065438455231
0.1%
0.065177319091
0.1%

AMZN
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1445
Distinct (%)100.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.001138590435
Minimum-0.1241305917
Maximum0.08253502001
Zeros1
Zeros (%)0.1%
Negative799
Negative (%)55.3%
Memory size11.4 KiB
2022-01-08T12:50:04.237169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.1241305917
5-th percentile-0.02847075842
Q1-0.01024659972
median-0.001467072645
Q30.007073261157
95-th percentile0.02896489028
Maximum0.08253502001
Range0.2066656117
Interquartile range (IQR)0.01731986088

Descriptive statistics

Standard deviation0.01857524134
Coefficient of variation (CV)-16.31424327
Kurtosis4.652562939
Mean-0.001138590435
Median Absolute Deviation (MAD)0.008621280145
Skewness-0.07151986354
Sum-1.645263178
Variance0.0003450395908
MonotonicityNot monotonic
2022-01-08T12:50:04.367203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.010719214331
 
0.1%
0.00078027982161
 
0.1%
-0.0097252842581
 
0.1%
-0.0011621834711
 
0.1%
-0.012476764081
 
0.1%
-0.016570406591
 
0.1%
-0.0075244050851
 
0.1%
0.0008021434381
 
0.1%
-0.01169970711
 
0.1%
0.0053809731731
 
0.1%
Other values (1435)1435
99.2%
ValueCountFrequency (%)
-0.12413059171
0.1%
-0.091361025491
0.1%
-0.090254023931
0.1%
-0.085388682251
0.1%
-0.076308271721
0.1%
-0.071195658861
0.1%
-0.068487249581
0.1%
-0.067907285631
0.1%
-0.066339788371
0.1%
-0.062644526521
0.1%
ValueCountFrequency (%)
0.082535020011
0.1%
0.081423521451
0.1%
0.079151170551
0.1%
0.079015193881
0.1%
0.078663256631
0.1%
0.065760076471
0.1%
0.065353343811
0.1%
0.063498213431
0.1%
0.060900287711
0.1%
0.060446885631
0.1%

SMSN
Real number (ℝ)

ZEROS

Distinct1381
Distinct (%)95.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.0007907293135
Minimum-0.1293363543
Maximum0.1192695147
Zeros39
Zeros (%)2.7%
Negative749
Negative (%)51.8%
Memory size11.4 KiB
2022-01-08T12:50:04.499212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.1293363543
5-th percentile-0.03023818692
Q1-0.01258537849
median-0.0009629273734
Q30.009583560264
95-th percentile0.03061910327
Maximum0.1192695147
Range0.248605869
Interquartile range (IQR)0.02216893876

Descriptive statistics

Standard deviation0.01908384989
Coefficient of variation (CV)-24.13449149
Kurtosis3.555500953
Mean-0.0007907293135
Median Absolute Deviation (MAD)0.01089311666
Skewness0.2491579956
Sum-1.142603858
Variance0.0003641933266
MonotonicityNot monotonic
2022-01-08T12:50:04.641206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
039
 
2.7%
0.019685675073
 
0.2%
-0.024292692572
 
0.1%
-0.0053238812532
 
0.1%
0.013838234672
 
0.1%
0.0058479698822
 
0.1%
-0.00096292737342
 
0.1%
-0.010313459132
 
0.1%
-0.0027816429622
 
0.1%
-0.0096931292062
 
0.1%
Other values (1371)1387
95.9%
ValueCountFrequency (%)
-0.12933635431
0.1%
-0.073445696111
0.1%
-0.061066806211
0.1%
-0.05800060781
0.1%
-0.055763574521
0.1%
-0.053120161821
0.1%
-0.052804144141
0.1%
-0.052442659751
0.1%
-0.051328406131
0.1%
-0.050700052761
0.1%
ValueCountFrequency (%)
0.11926951471
0.1%
0.10670189751
0.1%
0.085359848951
0.1%
0.070936926531
0.1%
0.066047361261
0.1%
0.061549507921
0.1%
0.055840823831
0.1%
0.054778206011
0.1%
0.052812271631
0.1%
0.051932748741
0.1%

HP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1406
Distinct (%)97.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.0005925590317
Minimum-0.1539649483
Maximum0.1896438973
Zeros32
Zeros (%)2.2%
Negative782
Negative (%)54.1%
Memory size11.4 KiB
2022-01-08T12:50:04.782207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.1539649483
5-th percentile-0.02964193214
Q1-0.01082987004
median-0.001581808883
Q30.007303684101
95-th percentile0.03428540866
Maximum0.1896438973
Range0.3436088456
Interquartile range (IQR)0.01813355414

Descriptive statistics

Standard deviation0.02190428756
Coefficient of variation (CV)-36.96557876
Kurtosis13.37089055
Mean-0.0005925590317
Median Absolute Deviation (MAD)0.00903458439
Skewness1.273939765
Sum-0.8562478008
Variance0.0004797978133
MonotonicityNot monotonic
2022-01-08T12:50:04.920208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032
 
2.2%
-0.00051639554382
 
0.1%
-0.0077220460942
 
0.1%
0.0054446595482
 
0.1%
-0.0057003005672
 
0.1%
0.000929800162
 
0.1%
0.0020843676672
 
0.1%
0.001398182472
 
0.1%
0.0024419547562
 
0.1%
0.016711075271
 
0.1%
Other values (1396)1396
96.5%
ValueCountFrequency (%)
-0.15396494831
0.1%
-0.12150683181
0.1%
-0.093852908811
0.1%
-0.082887598081
0.1%
-0.081387759821
0.1%
-0.066585604761
0.1%
-0.065567183621
0.1%
-0.062238036981
0.1%
-0.061647116791
0.1%
-0.05968042981
0.1%
ValueCountFrequency (%)
0.18964389731
0.1%
0.17235969981
0.1%
0.15684252911
0.1%
0.13153078351
0.1%
0.12152665851
0.1%
0.10054128931
0.1%
0.10043698721
0.1%
0.093852908811
0.1%
0.093660388711
0.1%
0.075611981051
0.1%

TSLA
Real number (ℝ)

HIGH CORRELATION

Distinct1445
Distinct (%)100.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.0019802085
Minimum-0.1814450334
Maximum0.2365179182
Zeros1
Zeros (%)0.1%
Negative754
Negative (%)52.1%
Memory size11.4 KiB
2022-01-08T12:50:05.137204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.1814450334
5-th percentile-0.05674390148
Q1-0.01894382364
median-0.001345875049
Q30.01475153699
95-th percentile0.04980229746
Maximum0.2365179182
Range0.4179629515
Interquartile range (IQR)0.03369536064

Descriptive statistics

Standard deviation0.03591363874
Coefficient of variation (CV)-18.13629158
Kurtosis6.028409246
Mean-0.0019802085
Median Absolute Deviation (MAD)0.01694346748
Skewness0.1348480726
Sum-2.861401282
Variance0.001289789447
MonotonicityNot monotonic
2022-01-08T12:50:05.272202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0077814799981
 
0.1%
-0.0087691851351
 
0.1%
-0.0081268554711
 
0.1%
-0.037320611771
 
0.1%
0.020418706771
 
0.1%
-0.00095622289711
 
0.1%
0.0043483140761
 
0.1%
0.0021483909711
 
0.1%
-0.01697601811
 
0.1%
-0.087432992311
 
0.1%
Other values (1435)1435
99.2%
ValueCountFrequency (%)
-0.18144503341
0.1%
-0.17932716841
0.1%
-0.16879448761
0.1%
-0.16270738061
0.1%
-0.15996629461
0.1%
-0.15084589251
0.1%
-0.15003941611
0.1%
-0.1286187211
0.1%
-0.12754715041
0.1%
-0.12645110921
0.1%
ValueCountFrequency (%)
0.23651791821
0.1%
0.2055222621
0.1%
0.18845037441
0.1%
0.17476299591
0.1%
0.14967865261
0.1%
0.14634126271
0.1%
0.14586457421
0.1%
0.138930151
0.1%
0.13713309171
0.1%
0.1234932191
0.1%

IBM
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1440
Distinct (%)99.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-1.624974972 × 10-5
Minimum-0.107068458
Maximum0.1375477303
Zeros5
Zeros (%)0.3%
Negative757
Negative (%)52.4%
Memory size11.4 KiB
2022-01-08T12:50:05.414165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.107068458
5-th percentile-0.02216028473
Q1-0.007068594513
median-0.0004888182915
Q30.006520847675
95-th percentile0.02282791115
Maximum0.1375477303
Range0.2446161883
Interquartile range (IQR)0.01358944219

Descriptive statistics

Standard deviation0.01612981877
Coefficient of variation (CV)-992.6195204
Kurtosis10.81704711
Mean-1.624974972 × 10-5
Median Absolute Deviation (MAD)0.00675646838
Skewness0.7225586219
Sum-0.02348088834
Variance0.0002601710535
MonotonicityNot monotonic
2022-01-08T12:50:05.551205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
0.3%
0.0016371081422
 
0.1%
-0.0048340180861
 
0.1%
-0.0027829606141
 
0.1%
-0.0019610412871
 
0.1%
-0.015735498731
 
0.1%
-0.010093151621
 
0.1%
-0.0016371081421
 
0.1%
-0.020499241921
 
0.1%
-0.016022852461
 
0.1%
Other values (1430)1430
98.9%
ValueCountFrequency (%)
-0.1070684581
0.1%
-0.084933563581
0.1%
-0.081247436381
0.1%
-0.076724176661
0.1%
-0.07362484031
0.1%
-0.064390895691
0.1%
-0.058084880641
0.1%
-0.057398885161
0.1%
-0.049635856671
0.1%
-0.049129972751
0.1%
ValueCountFrequency (%)
0.13754773031
0.1%
0.10430608591
0.1%
0.095764583481
0.1%
0.085740552341
0.1%
0.08084550381
0.1%
0.079348121681
0.1%
0.078309286631
0.1%
0.067133770311
0.1%
0.058041061541
0.1%
0.057547998441
0.1%

APPLE
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1424
Distinct (%)98.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.001169942156
Minimum-0.1130811983
Maximum0.1377133873
Zeros16
Zeros (%)1.1%
Negative772
Negative (%)53.4%
Memory size11.4 KiB
2022-01-08T12:50:05.689203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.1130811983
5-th percentile-0.02806624796
Q1-0.01010554905
median-0.001013684831
Q30.006532203823
95-th percentile0.02727227422
Maximum0.1377133873
Range0.2507945856
Interquartile range (IQR)0.01663775288

Descriptive statistics

Standard deviation0.01868557034
Coefficient of variation (CV)-15.97136255
Kurtosis7.061804666
Mean-0.001169942156
Median Absolute Deviation (MAD)0.008393792128
Skewness0.3335488639
Sum-1.690566416
Variance0.0003491505388
MonotonicityNot monotonic
2022-01-08T12:50:05.838168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016
 
1.1%
-0.0063897980992
 
0.1%
-0.014231219932
 
0.1%
-0.010341353792
 
0.1%
-0.0083752583372
 
0.1%
0.028706065632
 
0.1%
0.028749413292
 
0.1%
0.030167521141
 
0.1%
-0.01256503831
 
0.1%
-0.0073577479741
 
0.1%
Other values (1414)1414
97.8%
ValueCountFrequency (%)
-0.11308119831
0.1%
-0.099563518861
0.1%
-0.095650483971
0.1%
-0.088984845451
0.1%
-0.083719579991
0.1%
-0.06951372571
0.1%
-0.067920836061
0.1%
-0.066040495791
0.1%
-0.062934007451
0.1%
-0.061584688641
0.1%
ValueCountFrequency (%)
0.13771338731
0.1%
0.10485405831
0.1%
0.10397379891
0.1%
0.083447788091
0.1%
0.082467455131
0.1%
0.069666284121
0.1%
0.068722929521
0.1%
0.067850666911
0.1%
0.067568439311
0.1%
0.065672060951
0.1%

INTEL
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1429
Distinct (%)98.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.0003137912504
Minimum-0.1783244118
Maximum0.1989574233
Zeros11
Zeros (%)0.8%
Negative753
Negative (%)52.1%
Memory size11.4 KiB
2022-01-08T12:50:05.995165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.1783244118
5-th percentile-0.02769173069
Q1-0.009681753785
median-0.0008186656208
Q30.007953544528
95-th percentile0.02963317716
Maximum0.1989574233
Range0.3772818351
Interquartile range (IQR)0.01763529831

Descriptive statistics

Standard deviation0.0209375713
Coefficient of variation (CV)-66.72452234
Kurtosis16.66475315
Mean-0.0003137912504
Median Absolute Deviation (MAD)0.008856993244
Skewness0.8389525609
Sum-0.4534283568
Variance0.0004383818918
MonotonicityNot monotonic
2022-01-08T12:50:06.149204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011
 
0.8%
0.0070719270992
 
0.1%
0.0057388966692
 
0.1%
-0.0008684325342
 
0.1%
0.0025740039952
 
0.1%
0.0076859192062
 
0.1%
0.00065854464022
 
0.1%
0.018490824511
 
0.1%
-0.00027177605821
 
0.1%
-0.023810648691
 
0.1%
Other values (1419)1419
98.1%
ValueCountFrequency (%)
-0.17832441181
0.1%
-0.11566367771
0.1%
-0.10031469431
0.1%
-0.080192760941
0.1%
-0.078648628091
0.1%
-0.078194458121
0.1%
-0.077875380381
0.1%
-0.076440895761
0.1%
-0.071167047971
0.1%
-0.067363696371
0.1%
ValueCountFrequency (%)
0.19895742331
0.1%
0.17723517661
0.1%
0.1260927271
0.1%
0.11177145691
0.1%
0.097457857451
0.1%
0.095432361981
0.1%
0.094217217161
0.1%
0.092355967911
0.1%
0.089803894861
0.1%
0.067517747171
0.1%

Interactions

2022-01-08T12:50:00.449164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:48.345715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:49.605715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:50.848747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:52.443713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:53.637166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:55.155166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:56.566164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:57.796166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:59.146164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:00.590165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:48.489717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:49.733713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:50.980750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:52.559752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:53.768165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:55.367200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:56.686165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:57.931165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:59.275166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:00.724200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:48.604716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:49.848746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:51.109747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:52.666752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:53.890166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:55.495166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:56.810165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:58.121165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:59.397165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:00.950192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:48.732746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:49.978748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:51.249749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:52.789720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:54.026210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:55.642198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:56.949165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:58.251200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:59.540166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:01.069167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:48.839713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:50.091717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:51.368748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:52.898713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:54.144198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:55.767201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:57.060198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:58.360166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:59.658168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:01.209198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:48.949747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:50.211713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:51.502751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:53.015747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:54.294165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:55.894198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:57.173166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:58.484165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:59.780169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:01.348165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:49.076753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:50.358716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:51.644713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:53.145713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:54.454165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:56.036197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:57.311165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:58.616164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:59.927165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:01.489166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-01-08T12:49:56.162164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-01-08T12:49:49.361752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:50.589714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:51.918747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:53.377717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:54.724169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:56.287165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:57.544166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:58.889165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:00.175167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:01.765165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:49.477746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:50.710748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:52.266719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:53.505165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:54.996164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:56.423199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:57.659164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:49:59.007167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-08T12:50:00.310168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-01-08T12:50:06.300201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-08T12:50:06.518204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-08T12:50:06.725204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-08T12:50:07.028199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-08T12:50:02.005169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-08T12:50:02.272165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-08T12:50:02.502203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-08T12:50:02.783165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateMSFTGOOGFBAMZNSMSNHPTSLAIBMAPPLEINTEL
02016-01-04NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12016-01-05-0.004552-0.000997-0.0049770.005036-0.008889-0.009438-0.0000900.0007360.0253760.004718
22016-01-060.018332-0.001400-0.0023330.0018000.0350210.0365260.0198440.0050180.0200600.022419
32016-01-070.0354020.0234430.0502870.0398410.0100300.0471530.0155980.0172370.0430260.038206
42016-01-08-0.0030620.0165460.0060440.0014650.0136700.0177990.0217990.009301-0.0053770.010418
52016-01-110.000574-0.002181-0.001848-0.0174570.004180-0.0065940.015041-0.012082-0.015961-0.017304
62016-01-12-0.009136-0.013924-0.018895-0.0002430.005249-0.015836-0.0101480.002480-0.014511-0.019154
72016-01-130.0218360.0357660.0403520.0601670.0014750.0224310.0470980.0131030.0259440.023844
82016-01-14-0.028069-0.020011-0.030238-0.0190500.009107-0.003774-0.028883-0.013178-0.021532-0.025678
92016-01-150.0407360.0287710.0351750.0392420.0346350.0492140.0057880.0219070.0244110.095432

Last rows

DateMSFTGOOGFBAMZNSMSNHPTSLAIBMAPPLEINTEL
14362021-09-16-0.0013110.0057500.002303-0.0035760.018432-0.003215-0.0015340.0056280.0016120.005275
14372021-09-170.0176840.0203620.0226090.0074010.0101150.012922-0.0032970.0088350.0185180.010450
14382021-09-200.0187490.0174450.0250420.0313270.0058710.0160240.0393700.0068260.0215930.023873
14392021-09-21-0.001698-0.004518-0.0049920.0036120.0012400.015166-0.0125350.010027-0.0034220.002078
14402021-09-22-0.012741-0.0092090.040737-0.010834-0.013866-0.027933-0.016845-0.012407-0.016732-0.011846
14412021-09-23-0.003277-0.006281-0.007981-0.010580-0.003360-0.014037-0.002258-0.015478-0.006697-0.009858
14422021-09-240.000701-0.005670-0.020032-0.002783-0.0006100.003222-0.027161-0.005543-0.000613-0.003510
14432021-09-270.0174560.007968-0.0017550.0057730.013501-0.028977-0.021677-0.0077520.010606-0.008082
14442021-09-280.0368750.0383000.0372540.026733-0.0037000.0045380.0175920.0078980.0240890.012148
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